A platform team lead is responsible for automating the individual teams attribution towards SQL Warehouse usage. The requirement is to identify the SQL warehouse usage at the individual user's level and generate a daily report to be shared with an executive team that includes leaders from all business units. How should the platform lead generate an automated report that can be shared daily?

• A. Use the system tables to capture the audit and billing usage data and create a dashboard with daily refresh schedules and shared with the executive team.
• B. Let the users run the SQL query and then directly report the usage to the executives. The ownership of the SQL warehouse usage will be with the individual teams.
• C. Restrict users from running any SQL query unless they provide all the query details so that the attribution can be calculated and shared with the executive team.
• D. Use the system tables to capture the audit and billing usage data and share the queries with the executive team. This enables the executives to execute the query and see the latest results any time.

An upstream system is emitting change data capture (CDC) logs that are being written to a cloud object storage directory. Each record in the log indicates the change type (insert, update, or delete) and the values for each field after the change. The source table has a primary key identified by the field pk_id.
For auditing purposes, the data governance team wishes to maintain a full record of all values that have ever been valid in the source system. For analytical purposes, only the most recent value for each record needs to be recorded. The Databricks job to ingest these records occurs once per hour, but each individual record may have changed multiple times over the course of an hour.
Which solution meets these requirements?

• A. Create a separate history table for each pk_id resolve the current state of the table by running a union all filtering the history tables for the most recent state.
• B. Use merge into to insert, update, or delete the most recent entry for each pk_id into a bronze table, then propagate all changes throughout the system.
• C. Iterate through an ordered set of changes to the table, applying each in turn; rely on Delta Lake's versioning ability to create an audit log.
• D. Ingest all log information into a bronze table; use merge into to insert, update, or delete the most recent entry for each pk_id into a silver table to recreate the current table state.
• E. Use Delta Lake's change data feed to automatically process CDC data from an external system, propagating all changes to all dependent tables in the Lakehouse.

A data engineer has created a new cluster using shared access mode with default configurations.
The data engineer needs to allow the development team access to view the driver logs if needed.
What are the minimal cluster permissions that allow the development team to accomplish this?

• A. CAN ATTACH TO
• B. CAN VIEW
• C. CAN RESTART
• D. CAN MANAGE

A data engineer is configuring a Databricks Asset Bundle to deploy a job with granular permissions.
The requirements are:
- Grant the data-engineers group CAN_MANAGE access to the job.
- Ensure the auditors' group can view the job but not modify/run it.
- Avoid granting unintended permissions to other users/groups.
How should the data engineer deploy the job while meeting the requirements?

• A. resources:
  jobs:
  my-job:
  name: data-pipeline
  tasks: [...]
  job_clusters: [...]
  permissions:
  - group_name: data-engineers
  level: CAN_MANAGE
  - group_name: auditors
  level: CAN_VIEW
  - group_name: admin-team
  level: IS_OWNER
• B. permissions:
  - group_name: data-engineers
  level: CAN_MANAGE
  - group_name: auditors
  level: CAN_VIEW
  resources:
  jobs:
  my-job:
  name: data-pipeline
  tasks: [...]
  job_clusters: [...]
• C. resources:
  jobs:
  my-job:
  name: data-pipeline
  tasks: [...]
  job: [...]
  permissions:
  - group_name: data-engineers
  level: CAN_MANAGE
  permissions:
  - group_name: auditors
  level: CAN_VIEW
• D. resources:
  jobs:
  my-job:
  name: data-pipeline
  tasks: [...]
  job_clusters: [...]
  permissions:
  - group_name: data-engineers
  level: CAN_MANAGE
  - group_name: auditors
  level: CAN_VIEW

A Structured Streaming job deployed to production has been resulting in higher than expected cloud storage costs. At present, during normal execution, each microbatch of data is processed in less than 3s; at least 12 times per minute, a microbatch is processed that contains 0 records. The streaming write was configured using the default trigger settings. The production job is currently scheduled alongside many other Databricks jobs in a workspace with instance pools provisioned to reduce start-up time for jobs with batch execution.
Holding all other variables constant and assuming records need to be processed in less than 10 minutes, which adjustment will meet the requirement?

• A. Set the trigger interval to 3 seconds; the default trigger interval is consuming too many records per batch, resulting in spill to disk that can increase volume costs.
• B. Use the trigger once option and configure a Databricks job to execute the query every 10 minutes; this approach minimizes costs for both compute and storage.
• C. Set the trigger interval to 10 minutes; each batch calls APIs in the source storage account, so decreasing trigger frequency to maximum allowable threshold should minimize this cost.
• D. Set the trigger interval to 500 milliseconds; setting a small but non-zero trigger interval ensures that the source is not queried too frequently.
• E. Increase the number of shuffle partitions to maximize parallelism, since the trigger interval cannot be modified without modifying the checkpoint directory.